In recent years, research on detecting a person, using an image captured by a camera has been conducted. In particular, because the outline of the head and shoulders of a person represents a characteristic shape of a person, attention has been focused on detecting a person, using an edge-based feature value of this outline. The outline of the head and shoulders of a person resembles the omega (Ω) symbol of the Greek alphabet, so that the outline is called an “omega figure” or an “omega shape,” for example.
A technology described in Non-Patent Literature (hereinafter, abbreviated as “NPL”) 1, (the technology is referred to as “related art,” hereinafter) focuses on the omega shape in detecting a person from an image. The related art uses a Boosting method such as Real AdaBoost to learn tendencies of feature values of images including the omega shape from several thousands to several tens of thousands of sample images. Based on this learning, the related art generates a classifier that classifies images between images including the omega shape and images not including the omega shape. Examples of feature values include HoG (histogram of gradient) feature values, Sparse feature values and Haar feature values. Examples of learning methods include a Boosting method, an SVM (support vector machine) and a neural network.
According to the related art as described above, it is possible to determine at which positions the head and shoulders of a person are located in an image based on low-dimensional information with a small processing load.